[ted id=2243 lang=en]
Nick Bostrom: What happens when our computers get smarter than we are?
I work with a bunch of mathematicians, philosophers and computer scientists, and we sit around and think about the future of machine intelligence, among other things. Some people think that some of these things are sort of science fiction-y, far out there, crazy. But I like to say, okay, let’s look at the modern human condition. (Laughter) This is the normal way for things to be.
But if we think about it, we are actually recently arrived guests on this planet, the human species. Think about if Earth was created one year ago, the human species, then, would be 10 minutes old. The industrial era started two seconds ago. Another way to look at this is to think of world GDP over the last 10,000 years, I’ve actually taken the trouble to plot this for you in a graph. It looks like this. (Laughter) It’s a curious shape for a normal condition. I sure wouldn’t want to sit on it. (Laughter)
Let’s ask ourselves, what is the cause of this current anomaly? Some people would say it’s technology. Now it’s true, technology has accumulated through human history, and right now, technology advances extremely rapidly — that is the proximate cause, that’s why we are currently so very productive. But I like to think back further to the ultimate cause.
Look at these two highly distinguished gentlemen: We have Kanzi — he’s mastered 200 lexical tokens, an incredible feat. And Ed Witten unleashed the second superstring revolution. If we look under the hood, this is what we find: basically the same thing. One is a little larger, it maybe also has a few tricks in the exact way it’s wired. These invisible differences cannot be too complicated, however, because there have only been 250,000 generations since our last common ancestor. We know that complicated mechanisms take a long time to evolve. So a bunch of relatively minor changes take us from Kanzi to Witten, from broken-off tree branches to intercontinental ballistic missiles.
So this then seems pretty obvious that everything we’ve achieved, and everything we care about, depends crucially on some relatively minor changes that made the human mind. And the corollary, of course, is that any further changes that could significantly change the substrate of thinking could have potentially enormous consequences.
Some of my colleagues think we’re on the verge of something that could cause a profound change in that substrate, and that is machine superintelligence. Artificial intelligence used to be about putting commands in a box. You would have human programmers that would painstakingly handcraft knowledge items. You build up these expert systems, and they were kind of useful for some purposes, but they were very brittle, you couldn’t scale them. Basically, you got out only what you put in. But since then, a paradigm shift has taken place in the field of artificial intelligence.
Today, the action is really around machine learning. So rather than handcrafting knowledge representations and features, we create algorithms that learn, often from raw perceptual data. Basically the same thing that the human infant does. The result is A.I. that is not limited to one domain — the same system can learn to translate between any pairs of languages, or learn to play any computer game on the Atari console. Now of course, A.I. is still nowhere near having the same powerful, cross-domain ability to learn and plan as a human being has. The cortex still has some algorithmic tricks that we don’t yet know how to match in machines.
So the question is, how far are we from being able to match those tricks? A couple of years ago, we did a survey of some of the world’s leading A.I. experts, to see what they think, and one of the questions we asked was, “By which year do you think there is a 50 percent probability that we will have achieved human-level machine intelligence?” We defined human-level here as the ability to perform almost any job at least as well as an adult human, so real human-level, not just within some limited domain. And the median answer was 2040 or 2050, depending on precisely which group of experts we asked. Now, it could happen much, much later, or sooner, the truth is nobody really knows.
What we do know is that the ultimate limit to information processing in a machine substrate lies far outside the limits in biological tissue. This comes down to physics. A biological neuron fires, maybe, at 200 hertz, 200 times a second. But even a present-day transistor operates at the Gigahertz. Neurons propagate slowly in axons, 100 meters per second, tops. But in computers, signals can travel at the speed of light. There are also size limitations, like a human brain has to fit inside a cranium, but a computer can be the size of a warehouse or larger. So the potential for superintelligence lies dormant in matter, much like the power of the atom lay dormant throughout human history, patiently waiting there until 1945. In this century, scientists may learn to awaken the power of artificial intelligence. And I think we might then see an intelligence explosion.
Now most people, when they think about what is smart and what is dumb, I think have in mind a picture roughly like this. So at one end we have the village idiot, and then far over at the other side we have Ed Witten, or Albert Einstein, or whoever your favorite guru is. But I think that from the point of view of artificial intelligence, the true picture is actually probably more like this: AI starts out at this point here, at zero intelligence, and then, after many, many years of really hard work, maybe eventually we get to mouse-level artificial intelligence, something that can navigate cluttered environments as well as a mouse can. And then, after many, many more years of really hard work, lots of investment, maybe eventually we get to chimpanzee-level artificial intelligence. And then, after even more years of really, really hard work, we get to village idiot artificial intelligence. And a few moments later, we are beyond Ed Witten. The train doesn’t stop at Humanville Station. It’s likely, rather, to swoosh right by.
Now this has profound implications, particularly when it comes to questions of power. For example, chimpanzees are strong — pound for pound, a chimpanzee is about twice as strong as a fit human male. And yet, the fate of Kanzi and his pals depends a lot more on what we humans do than on what the chimpanzees do themselves. Once there is superintelligence, the fate of humanity may depend on what the superintelligence does. Think about it: Machine intelligence is the last invention that humanity will ever need to make. Machines will then be better at inventing than we are, and they’ll be doing so on digital timescales. What this means is basically a telescoping of the future. Think of all the crazy technologies that you could have imagined maybe humans could have developed in the fullness of time: cures for aging, space colonization, self-replicating nanobots or uploading of minds into computers, all kinds of science fiction-y stuff that’s nevertheless consistent with the laws of physics. All of this superintelligence could develop, and possibly quite rapidly.
Now, a superintelligence with such technological maturity would be extremely powerful, and at least in some scenarios, it would be able to get what it wants. We would then have a future that would be shaped by the preferences of this A.I. Now a good question is, what are those preferences? Here it gets trickier. To make any headway with this, we must first of all avoid anthropomorphizing. And this is ironic because every newspaper article about the future of A.I. has a picture of this: So I think what we need to do is to conceive of the issue more abstractly, not in terms of vivid Hollywood scenarios.
We need to think of intelligence as an optimization process, a process that steers the future into a particular set of configurations. A superintelligence is a really strong optimization process. It’s extremely good at using available means to achieve a state in which its goal is realized. This means that there is no necessary conenction between being highly intelligent in this sense, and having an objective that we humans would find worthwhile or meaningful.
Suppose we give an A.I. the goal to make humans smile. When the A.I. is weak, it performs useful or amusing actions that cause its user to smile. When the A.I. becomes superintelligent, it realizes that there is a more effective way to achieve this goal: take control of the world and stick electrodes into the facial muscles of humans to cause constant, beaming grins. Another example, suppose we give A.I. the goal to solve a difficult mathematical problem. When the A.I. becomes superintelligent, it realizes that the most effective way to get the solution to this problem is by transforming the planet into a giant computer, so as to increase its thinking capacity. And notice that this gives the A.I.s an instrumental reason to do things to us that we might not approve of. Human beings in this model are threats, we could prevent the mathematical problem from being solved.
Of course, perceivably things won’t go wrong in these particular ways; these are cartoon examples. But the general point here is important: if you create a really powerful optimization process to maximize for objective x, you better make sure that your definition of x incorporates everything you care about. This is a lesson that’s also taught in many a myth. King Midas wishes that everything he touches be turned into gold. He touches his daughter, she turns into gold. He touches his food, it turns into gold. This could become practically relevant, not just as a metaphor for greed, but as an illustration of what happens if you create a powerful optimization process and give it misconceived or poorly specified goals.
Now you might say, if a computer starts sticking electrodes into people’s faces, we’d just shut it off. A, this is not necessarily so easy to do if we’ve grown dependent on the system — like, where is the off switch to the Internet? B, why haven’t the chimpanzees flicked the off switch to humanity, or the Neanderthals? They certainly had reasons. We have an off switch, for example, right here. (Choking) The reason is that we are an intelligent adversary; we can anticipate threats and plan around them. But so could a superintelligent agent, and it would be much better at that than we are. The point is, we should not be confident that we have this under control here.
And we could try to make our job a little bit easier by, say, putting the A.I. in a box, like a secure software environment, a virtual reality simulation from which it cannot escape. But how confident can we be that the A.I. couldn’t find a bug. Given that merely human hackers find bugs all the time, I’d say, probably not very confident. So we disconnect the ethernet cable to create an air gap, but again, like merely human hackers routinely transgress air gaps using social engineering. Right now, as I speak, I’m sure there is some employee out there somewhere who has been talked into handing out her account details by somebody claiming to be from the I.T. department.
More creative scenarios are also possible, like if you’re the A.I., you can imagine wiggling electrodes around in your internal circuitry to create radio waves that you can use to communicate. Or maybe you could pretend to malfunction, and then when the programmers open you up to see what went wrong with you, they look at the source code — Bam! — the manipulation can take place. Or it could output the blueprint to a really nifty technology, and when we implement it, it has some surreptitious side effect that the A.I. had planned. The point here is that we should not be confident in our ability to keep a superintelligent genie locked up in its bottle forever. Sooner or later, it will get out.
I believe that the answer here is to figure out how to create superintelligent A.I. such that even if — when — it escapes, it is still safe because it is fundamentally on our side because it shares our values. I see no way around this difficult problem.
Now, I’m actually fairly optimistic that this problem can be solved. We wouldn’t have to write down a long list of everything we care about, or worse yet, spell it out in some computer language like C++ or Python, that would be a task beyond hopeless. Instead, we would create an A.I. that uses its intelligence to learn what we value, and its motivation system is constructed in such a way that it is motivated to pursue our values or to perform actions that it predicts we would approve of. We would thus leverage its intelligence as much as possible to solve the problem of value-loading.
This can happen, and the outcome could be very good for humanity. But it doesn’t happen automatically. The initial conditions for the intelligence explosion might need to be set up in just the right way if we are to have a controlled detonation. The values that the A.I. has need to match ours, not just in the familiar context, like where we can easily check how the A.I. behaves, but also in all novel contexts that the A.I. might encounter in the indefinite future.
And there are also some esoteric issues that would need to be solved, sorted out: the exact details of its decision theory, how to deal with logical uncertainty and so forth. So the technical problems that need to be solved to make this work look quite difficult — not as difficult as making a superintelligent A.I., but fairly difficult. Here is the worry: Making superintelligent A.I. is a really hard challenge. Making superintelligent A.I. that is safe involves some additional challenge on top of that. The risk is that if somebody figures out how to crack the first challenge without also having cracked the additional challenge of ensuring perfect safety.
So I think that we should work out a solution to the control problem in advance, so that we have it available by the time it is needed. Now it might be that we cannot solve the entire control problem in advance because maybe some elements can only be put in place once you know the details of the architecture where it will be implemented. But the more of the control problem that we solve in advance, the better the odds that the transition to the machine intelligence era will go well.
This to me looks like a thing that is well worth doing and I can imagine that if things turn out okay, that people a million years from now look back at this century and it might well be that they say that the one thing we did that really mattered was to get this thing right.
[ted id=2592 lang=en]
Scared of superintelligent AI? You should be, says neuroscientist and philosopher Sam Harris — and not just in some theoretical way. We’re going to build superhuman machines, says Harris, but we haven’t yet grappled with the problems associated with creating something that may treat us the way we treat ants.
Sam Harris: Can we build AI without losing control over it?
I’m going to talk about a failure of intuition that many of us suffer from. It’s really a failure to detect a certain kind of danger. I’m going to describe a scenario that I think is both terrifying and likely to occur, and that’s not a good combination, as it turns out. And yet rather than be scared, most of you will feel that what I’m talking about is kind of cool.
I’m going to describe how the gains we make in artificial intelligence could ultimately destroy us. And in fact, I think it’s very difficult to see how they won’t destroy us or inspire us to destroy ourselves. And yet if you’re anything like me, you’ll find that it’s fun to think about these things. And that response is part of the problem. OK? That response should worry you. And if I were to convince you in this talk that we were likely to suffer a global famine, either because of climate change or some other catastrophe, and that your grandchildren, or their grandchildren, are very likely to live like this, you wouldn’t think, “Interesting. I like this TED Talk.”
Famine isn’t fun. Death by science fiction, on the other hand, is fun, and one of the things that worries me most about the development of AI at this point is that we seem unable to marshal an appropriate emotional response to the dangers that lie ahead. I am unable to marshal this response, and I’m giving this talk.
It’s as though we stand before two doors. Behind door number one, we stop making progress in building intelligent machines. Our computer hardware and software just stops getting better for some reason. Now take a moment to consider why this might happen. I mean, given how valuable intelligence and automation are, we will continue to improve our technology if we are at all able to. What could stop us from doing this? A full-scale nuclear war? A global pandemic? An asteroid impact? Justin Bieber becoming president of the United States?
The point is, something would have to destroy civilization as we know it. You have to imagine how bad it would have to be to prevent us from making improvements in our technology permanently, generation after generation. Almost by definition, this is the worst thing that’s ever happened in human history.
So the only alternative, and this is what lies behind door number two, is that we continue to improve our intelligent machines year after year after year. At a certain point, we will build machines that are smarter than we are, and once we have machines that are smarter than we are, they will begin to improve themselves. And then we risk what the mathematician IJ Good called an “intelligence explosion,” that the process could get away from us.
Now, this is often caricatured, as I have here, as a fear that armies of malicious robots will attack us. But that isn’t the most likely scenario. It’s not that our machines will become spontaneously malevolent. The concern is really that we will build machines that are so much more competent than we are that the slightest divergence between their goals and our own could destroy us.
Just think about how we relate to ants. We don’t hate them. We don’t go out of our way to harm them. In fact, sometimes we take pains not to harm them. We step over them on the sidewalk. But whenever their presence seriously conflicts with one of our goals, let’s say when constructing a building like this one, we annihilate them without a qualm. The concern is that we will one day build machines that, whether they’re conscious or not, could treat us with similar disregard.
Now, I suspect this seems far-fetched to many of you. I bet there are those of you who doubt that superintelligent AI is possible, much less inevitable. But then you must find something wrong with one of the following assumptions. And there are only three of them.
Intelligence is a matter of information processing in physical systems. Actually, this is a little bit more than an assumption. We have already built narrow intelligence into our machines, and many of these machines perform at a level of superhuman intelligence already. And we know that mere matter can give rise to what is called “general intelligence,” an ability to think flexibly across multiple domains, because our brains have managed it. Right? I mean, there’s just atoms in here, and as long as we continue to build systems of atoms that display more and more intelligent behavior, we will eventually, unless we are interrupted, we will eventually build general intelligence into our machines.
It’s crucial to realize that the rate of progress doesn’t matter, because any progress is enough to get us into the end zone. We don’t need Moore’s law to continue. We don’t need exponential progress. We just need to keep going.
The second assumption is that we will keep going. We will continue to improve our intelligent machines. And given the value of intelligence — I mean, intelligence is either the source of everything we value or we need it to safeguard everything we value. It is our most valuable resource. So we want to do this. We have problems that we desperately need to solve. We want to cure diseases like Alzheimer’s and cancer. We want to understand economic systems. We want to improve our climate science. So we will do this, if we can. The train is already out of the station, and there’s no brake to pull.
Finally, we don’t stand on a peak of intelligence, or anywhere near it, likely. And this really is the crucial insight. This is what makes our situation so precarious, and this is what makes our intuitions about risk so unreliable.
Now, just consider the smartest person who has ever lived. On almost everyone’s shortlist here is John von Neumann. I mean, the impression that von Neumann made on the people around him, and this included the greatest mathematicians and physicists of his time, is fairly well-documented. If only half the stories about him are half true, there’s no question he’s one of the smartest people who has ever lived. So consider the spectrum of intelligence. Here we have John von Neumann. And then we have you and me. And then we have a chicken.
Sorry, a chicken.
There’s no reason for me to make this talk more depressing than it needs to be.
It seems overwhelmingly likely, however, that the spectrum of intelligence extends much further than we currently conceive, and if we build machines that are more intelligent than we are, they will very likely explore this spectrum in ways that we can’t imagine, and exceed us in ways that we can’t imagine.
And it’s important to recognize that this is true by virtue of speed alone. Right? So imagine if we just built a superintelligent AI that was no smarter than your average team of researchers at Stanford or MIT. Well, electronic circuits function about a million times faster than biochemical ones, so this machine should think about a million times faster than the minds that built it. So you set it running for a week, and it will perform 20,000 years of human-level intellectual work, week after week after week. How could we even understand, much less constrain, a mind making this sort of progress?
The other thing that’s worrying, frankly, is that, imagine the best case scenario. So imagine we hit upon a design of superintelligent AI that has no safety concerns. We have the perfect design the first time around. It’s as though we’ve been handed an oracle that behaves exactly as intended. Well, this machine would be the perfect labor-saving device. It can design the machine that can build the machine that can do any physical work, powered by sunlight, more or less for the cost of raw materials. So we’re talking about the end of human drudgery. We’re also talking about the end of most intellectual work.
So what would apes like ourselves do in this circumstance? Well, we’d be free to play Frisbee and give each other massages. Add some LSD and some questionable wardrobe choices, and the whole world could be like Burning Man.
Now, that might sound pretty good, but ask yourself what would happen under our current economic and political order? It seems likely that we would witness a level of wealth inequality and unemployment that we have never seen before. Absent a willingness to immediately put this new wealth to the service of all humanity, a few trillionaires could grace the covers of our business magazines while the rest of the world would be free to starve.
And what would the Russians or the Chinese do if they heard that some company in Silicon Valley was about to deploy a superintelligent AI? This machine would be capable of waging war, whether terrestrial or cyber, with unprecedented power. This is a winner-take-all scenario. To be six months ahead of the competition here is to be 500,000 years ahead, at a minimum. So it seems that even mere rumors of this kind of breakthrough could cause our species to go berserk.
Now, one of the most frightening things, in my view, at this moment, are the kinds of things that AI researchers say when they want to be reassuring. And the most common reason we’re told not to worry is time. This is all a long way off, don’t you know. This is probably 50 or 100 years away. One researcher has said, “Worrying about AI safety is like worrying about overpopulation on Mars.” This is the Silicon Valley version of “don’t worry your pretty little head about it.”
No one seems to notice that referencing the time horizon is a total non sequitur. If intelligence is just a matter of information processing, and we continue to improve our machines, we will produce some form of superintelligence. And we have no idea how long it will take us to create the conditions to do that safely. Let me say that again. We have no idea how long it will take us to create the conditions to do that safely.
And if you haven’t noticed, 50 years is not what it used to be. This is 50 years in months. This is how long we’ve had the iPhone. This is how long “The Simpsons” has been on television. Fifty years is not that much time to meet one of the greatest challenges our species will ever face. Once again, we seem to be failing to have an appropriate emotional response to what we have every reason to believe is coming.
The computer scientist Stuart Russell has a nice analogy here. He said, imagine that we received a message from an alien civilization, which read: “People of Earth, we will arrive on your planet in 50 years. Get ready.” And now we’re just counting down the months until the mothership lands? We would feel a little more urgency than we do.
Another reason we’re told not to worry is that these machines can’t help but share our values because they will be literally extensions of ourselves. They’ll be grafted onto our brains, and we’ll essentially become their limbic systems. Now take a moment to consider that the safest and only prudent path forward, recommended, is to implant this technology directly into our brains. Now, this may in fact be the safest and only prudent path forward, but usually one’s safety concerns about a technology have to be pretty much worked out before you stick it inside your head.
The deeper problem is that building superintelligent AI on its own seems likely to be easier than building superintelligent AI and having the completed neuroscience that allows us to seamlessly integrate our minds with it. And given that the companies and governments doing this work are likely to perceive themselves as being in a race against all others, given that to win this race is to win the world, provided you don’t destroy it in the next moment, then it seems likely that whatever is easier to do will get done first.
Now, unfortunately, I don’t have a solution to this problem, apart from recommending that more of us think about it. I think we need something like a Manhattan Project on the topic of artificial intelligence. Not to build it, because I think we’ll inevitably do that, but to understand how to avoid an arms race and to build it in a way that is aligned with our interests. When you’re talking about superintelligent AI that can make changes to itself, it seems that we only have one chance to get the initial conditions right, and even then we will need to absorb the economic and political consequences of getting them right.
But the moment we admit that information processing is the source of intelligence, that some appropriate computational system is what the basis of intelligence is, and we admit that we will improve these systems continuously, and we admit that the horizon of cognition very likely far exceeds what we currently know, then we have to admit that we are in the process of building some sort of god. Now would be a good time to make sure it’s a god we can live with.
Thank you very much.
[ted id=2155 lang=en]
What happens when we teach a computer how to learn? Technologist Jeremy Howard shares some surprising new developments in the fast-moving field of deep learning, a technique that can give computers the ability to learn Chinese, or to recognize objects in photos, or to help think through a medical diagnosis. (One deep learning tool, after watching hours of YouTube, taught itself the concept of “cats.”) Get caught up on a field that will change the way the computers around you behave … sooner than you probably think.
Jeremy Howard: The wonderful and terrifying implications of computers that can learn
It used to be that if you wanted to get a computer to do something new, you would have to program it. Now, programming, for those of you here that haven’t done it yourself, requires laying out in excruciating detail every single step that you want the computer to do in order to achieve your goal. Now, if you want to do something that you don’t know how to do yourself, then this is going to be a great challenge.
So this was the challenge faced by this man, Arthur Samuel. In 1956, he wanted to get this computer to be able to beat him at checkers. How can you write a program, lay out in excruciating detail, how to be better than you at checkers? So he came up with an idea: he had the computer play against itself thousands of times and learn how to play checkers. And indeed it worked, and in fact, by 1962, this computer had beaten the Connecticut state champion.
So Arthur Samuel was the father of machine learning, and I have a great debt to him, because I am a machine learning practitioner. I was the president of Kaggle, a community of over 200,000 machine learning practictioners. Kaggle puts up competitions to try and get them to solve previously unsolved problems, and it’s been successful hundreds of times. So from this vantage point, I was able to find out a lot about what machine learning can do in the past, can do today, and what it could do in the future. Perhaps the first big success of machine learning commercially was Google. Google showed that it is possible to find information by using a computer algorithm, and this algorithm is based on machine learning. Since that time, there have been many commercial successes of machine learning. Companies like Amazon and Netflix use machine learning to suggest products that you might like to buy, movies that you might like to watch. Sometimes, it’s almost creepy. Companies like LinkedIn and Facebook sometimes will tell you about who your friends might be and you have no idea how it did it, and this is because it’s using the power of machine learning. These are algorithms that have learned how to do this from data rather than being programmed by hand.
This is also how IBM was successful in getting Watson to beat the two world champions at “Jeopardy,” answering incredibly subtle and complex questions like this one. [“The ancient ‘Lion of Nimrud’ went missing from this city’s national museum in 2003 (along with a lot of other stuff)”] This is also why we are now able to see the first self-driving cars. If you want to be able to tell the difference between, say, a tree and a pedestrian, well, that’s pretty important. We don’t know how to write those programs by hand, but with machine learning, this is now possible. And in fact, this car has driven over a million miles without any accidents on regular roads.
So we now know that computers can learn, and computers can learn to do things that we actually sometimes don’t know how to do ourselves, or maybe can do them better than us. One of the most amazing examples I’ve seen of machine learning happened on a project that I ran at Kaggle where a team run by a guy called Geoffrey Hinton from the University of Toronto won a competition for automatic drug discovery. Now, what was extraordinary here is not just that they beat all of the algorithms developed by Merck or the international academic community, but nobody on the team had any background in chemistry or biology or life sciences, and they did it in two weeks. How did they do this? They used an extraordinary algorithm called deep learning. So important was this that in fact the success was covered in The New York Times in a front page article a few weeks later. This is Geoffrey Hinton here on the left-hand side. Deep learning is an algorithm inspired by how the human brain works, and as a result it’s an algorithm which has no theoretical limitations on what it can do. The more data you give it and the more computation time you give it, the better it gets.
The New York Times also showed in this article another extraordinary result of deep learning which I’m going to show you now. It shows that computers can listen and understand.
(Video) Richard Rashid: Now, the last step that I want to be able to take in this process is to actually speak to you in Chinese. Now the key thing there is, we’ve been able to take a large amount of information from many Chinese speakers and produce a text-to-speech system that takes Chinese text and converts it into Chinese language, and then we’ve taken an hour or so of my own voice and we’ve used that to modulate the standard text-to-speech system so that it would sound like me. Again, the result’s not perfect. There are in fact quite a few errors. (In Chinese) (Applause) There’s much work to be done in this area. (In Chinese) (Applause)
Jeremy Howard: Well, that was at a machine learning conference in China. It’s not often, actually, at academic conferences that you do hear spontaneous applause, although of course sometimes at TEDx conferences, feel free. Everything you saw there was happening with deep learning. (Applause) Thank you. The transcription in English was deep learning. The translation to Chinese and the text in the top right, deep learning, and the construction of the voice was deep learning as well.
So deep learning is this extraordinary thing. It’s a single algorithm that can seem to do almost anything, and I discovered that a year earlier, it had also learned to see. In this obscure competition from Germany called the German Traffic Sign Recognition Benchmark, deep learning had learned to recognize traffic signs like this one. Not only could it recognize the traffic signs better than any other algorithm, the leaderboard actually showed it was better than people, about twice as good as people. So by 2011, we had the first example of computers that can see better than people. Since that time, a lot has happened. In 2012, Google announced that they had a deep learning algorithm watch YouTube videos and crunched the data on 16,000 computers for a month, and the computer independently learned about concepts such as people and cats just by watching the videos. This is much like the way that humans learn. Humans don’t learn by being told what they see, but by learning for themselves what these things are. Also in 2012, Geoffrey Hinton, who we saw earlier, won the very popular ImageNet competition, looking to try to figure out from one and a half million images what they’re pictures of. As of 2014, we’re now down to a six percent error rate in image recognition. This is better than people, again.
So machines really are doing an extraordinarily good job of this, and it is now being used in industry. For example, Google announced last year that they had mapped every single location in France in two hours, and the way they did it was that they fed street view images into a deep learning algorithm to recognize and read street numbers. Imagine how long it would have taken before: dozens of people, many years. This is also happening in China. Baidu is kind of the Chinese Google, I guess, and what you see here in the top left is an example of a picture that I uploaded to Baidu’s deep learning system, and underneath you can see that the system has understood what that picture is and found similar images. The similar images actually have similar backgrounds, similar directions of the faces, even some with their tongue out. This is not clearly looking at the text of a web page. All I uploaded was an image. So we now have computers which really understand what they see and can therefore search databases of hundreds of millions of images in real time.
So what does it mean now that computers can see? Well, it’s not just that computers can see. In fact, deep learning has done more than that. Complex, nuanced sentences like this one are now understandable with deep learning algorithms. As you can see here, this Stanford-based system showing the red dot at the top has figured out that this sentence is expressing negative sentiment. Deep learning now in fact is near human performance at understanding what sentences are about and what it is saying about those things. Also, deep learning has been used to read Chinese, again at about native Chinese speaker level. This algorithm developed out of Switzerland by people, none of whom speak or understand any Chinese. As I say, using deep learning is about the best system in the world for this, even compared to native human understanding.
This is a system that we put together at my company which shows putting all this stuff together. These are pictures which have no text attached, and as I’m typing in here sentences, in real time it’s understanding these pictures and figuring out what they’re about and finding pictures that are similar to the text that I’m writing. So you can see, it’s actually understanding my sentences and actually understanding these pictures. I know that you’ve seen something like this on Google, where you can type in things and it will show you pictures, but actually what it’s doing is it’s searching the webpage for the text. This is very different from actually understanding the images. This is something that computers have only been able to do for the first time in the last few months.
So we can see now that computers can not only see but they can also read, and, of course, we’ve shown that they can understand what they hear. Perhaps not surprising now that I’m going to tell you they can write. Here is some text that I generated using a deep learning algorithm yesterday. And here is some text that an algorithm out of Stanford generated. Each of these sentences was generated by a deep learning algorithm to describe each of those pictures. This algorithm before has never seen a man in a black shirt playing a guitar. It’s seen a man before, it’s seen black before, it’s seen a guitar before, but it has independently generated this novel description of this picture. We’re still not quite at human performance here, but we’re close. In tests, humans prefer the computer-generated caption one out of four times. Now this system is now only two weeks old, so probably within the next year, the computer algorithm will be well past human performance at the rate things are going. So computers can also write.
So we put all this together and it leads to very exciting opportunities. For example, in medicine, a team in Boston announced that they had discovered dozens of new clinically relevant features of tumors which help doctors make a prognosis of a cancer. Very similarly, in Stanford, a group there announced that, looking at tissues under magnification, they’ve developed a machine learning-based system which in fact is better than human pathologists at predicting survival rates for cancer sufferers. In both of these cases, not only were the predictions more accurate, but they generated new insightful science. In the radiology case, they were new clinical indicators that humans can understand. In this pathology case, the computer system actually discovered that the cells around the cancer are as important as the cancer cells themselves in making a diagnosis. This is the opposite of what pathologists had been taught for decades. In each of those two cases, they were systems developed by a combination of medical experts and machine learning experts, but as of last year, we’re now beyond that too. This is an example of identifying cancerous areas of human tissue under a microscope. The system being shown here can identify those areas more accurately, or about as accurately, as human pathologists, but was built entirely with deep learning using no medical expertise by people who have no background in the field. Similarly, here, this neuron segmentation. We can now segment neurons about as accurately as humans can, but this system was developed with deep learning using people with no previous background in medicine.
So myself, as somebody with no previous background in medicine, I seem to be entirely well qualified to start a new medical company, which I did. I was kind of terrified of doing it, but the theory seemed to suggest that it ought to be possible to do very useful medicine using just these data analytic techniques. And thankfully, the feedback has been fantastic, not just from the media but from the medical community, who have been very supportive. The theory is that we can take the middle part of the medical process and turn that into data analysis as much as possible, leaving doctors to do what they’re best at. I want to give you an example. It now takes us about 15 minutes to generate a new medical diagnostic test and I’ll show you that in real time now, but I’ve compressed it down to three minutes by cutting some pieces out. Rather than showing you creating a medical diagnostic test, I’m going to show you a diagnostic test of car images, because that’s something we can all understand.
So here we’re starting with about 1.5 million car images, and I want to create something that can split them into the angle of the photo that’s being taken. So these images are entirely unlabeled, so I have to start from scratch. With our deep learning algorithm, it can automatically identify areas of structure in these images. So the nice thing is that the human and the computer can now work together. So the human, as you can see here, is telling the computer about areas of interest which it wants the computer then to try and use to improve its algorithm. Now, these deep learning systems actually are in 16,000-dimensional space, so you can see here the computer rotating this through that space, trying to find new areas of structure. And when it does so successfully, the human who is driving it can then point out the areas that are interesting. So here, the computer has successfully found areas, for example, angles. So as we go through this process, we’re gradually telling the computer more and more about the kinds of structures we’re looking for. You can imagine in a diagnostic test this would be a pathologist identifying areas of pathosis, for example, or a radiologist indicating potentially troublesome nodules. And sometimes it can be difficult for the algorithm. In this case, it got kind of confused. The fronts and the backs of the cars are all mixed up. So here we have to be a bit more careful, manually selecting these fronts as opposed to the backs, then telling the computer that this is a type of group that we’re interested in.
So we do that for a while, we skip over a little bit, and then we train the machine learning algorithm based on these couple of hundred things, and we hope that it’s gotten a lot better. You can see, it’s now started to fade some of these pictures out, showing us that it already is recognizing how to understand some of these itself. We can then use this concept of similar images, and using similar images, you can now see, the computer at this point is able to entirely find just the fronts of cars. So at this point, the human can tell the computer, okay, yes, you’ve done a good job of that.
Sometimes, of course, even at this point it’s still difficult to separate out groups. In this case, even after we let the computer try to rotate this for a while, we still find that the left sides and the right sides pictures are all mixed up together. So we can again give the computer some hints, and we say, okay, try and find a projection that separates out the left sides and the right sides as much as possible using this deep learning algorithm. And giving it that hint — ah, okay, it’s been successful. It’s managed to find a way of thinking about these objects that’s separated out these together.
So you get the idea here. This is a case not where the human is being replaced by a computer, but where they’re working together. What we’re doing here is we’re replacing something that used to take a team of five or six people about seven years and replacing it with something that takes 15 minutes for one person acting alone.
So this process takes about four or five iterations. You can see we now have 62 percent of our 1.5 million images classified correctly. And at this point, we can start to quite quickly grab whole big sections, check through them to make sure that there’s no mistakes. Where there are mistakes, we can let the computer know about them. And using this kind of process for each of the different groups, we are now up to an 80 percent success rate in classifying the 1.5 million images. And at this point, it’s just a case of finding the small number that aren’t classified correctly, and trying to understand why. And using that approach, by 15 minutes we get to 97 percent classification rates.
So this kind of technique could allow us to fix a major problem, which is that there’s a lack of medical expertise in the world. The World Economic Forum says that there’s between a 10x and a 20x shortage of physicians in the developing world, and it would take about 300 years to train enough people to fix that problem. So imagine if we can help enhance their efficiency using these deep learning approaches?
So I’m very excited about the opportunities. I’m also concerned about the problems. The problem here is that every area in blue on this map is somewhere where services are over 80 percent of employment. What are services? These are services. These are also the exact things that computers have just learned how to do. So 80 percent of the world’s employment in the developed world is stuff that computers have just learned how to do. What does that mean? Well, it’ll be fine. They’ll be replaced by other jobs. For example, there will be more jobs for data scientists. Well, not really. It doesn’t take data scientists very long to build these things. For example, these four algorithms were all built by the same guy. So if you think, oh, it’s all happened before, we’ve seen the results in the past of when new things come along and they get replaced by new jobs, what are these new jobs going to be? It’s very hard for us to estimate this, because human performance grows at this gradual rate, but we now have a system, deep learning, that we know actually grows in capability exponentially. And we’re here. So currently, we see the things around us and we say, “Oh, computers are still pretty dumb.” Right? But in five years’ time, computers will be off this chart. So we need to be starting to think about this capability right now.
We have seen this once before, of course. In the Industrial Revolution, we saw a step change in capability thanks to engines. The thing is, though, that after a while, things flattened out. There was social disruption, but once engines were used to generate power in all the situations, things really settled down. The Machine Learning Revolution is going to be very different from the Industrial Revolution, because the Machine Learning Revolution, it never settles down. The better computers get at intellectual activities, the more they can build better computers to be better at intellectual capabilities, so this is going to be a kind of change that the world has actually never experienced before, so your previous understanding of what’s possible is different.
This is already impacting us. In the last 25 years, as capital productivity has increased, labor productivity has been flat, in fact even a little bit down.
So I want us to start having this discussion now. I know that when I often tell people about this situation, people can be quite dismissive. Well, computers can’t really think, they don’t emote, they don’t understand poetry, we don’t really understand how they work. So what? Computers right now can do the things that humans spend most of their time being paid to do, so now’s the time to start thinking about how we’re going to adjust our social structures and economic structures to be aware of this new reality. Thank you. (Applause)
In “The Age of Intelligent Machines”, inventor and computer scientist Raymond Kurzweil probes the past, present, and future of artificial intelligence, from its earliest philosophical and mathematical roots to tantalizing glimpses of 21st-century machines with superior intelligence and prodigious speed and memory. This book provides the background needed for an understanding of the enormous scientific potential represented by intelligent machines as well as their equally profound philosophic, economic, and social implications. Running alongside Kurzweil’s historical and scientific narrative are 23 articles examining contemporary issues in artificial intelligence. This book won the Association of American Publishers Annual Award for Excellence in Professional and Scholarly Publishing. It contains articles by: Charles Ames; Margaret A. Boden; Harold Cohen; Caniel C. Dennett; Edward A. Feigenbaum; K. Fuchi; George Gilder; Douglas R. Hofstadter; Michael Lebowitz; Margaret Litven; Blaine Mathieu; Marvin Minsky; Allen Newell; Brian W. Oakley; Seymour Papert; Jeff Pepper; Roger Schank and Christopher Owens; Sherry Turkle; Mitchell Waldrop.
The Age of Intelligent Machines is a non-fiction book about artificial intelligence by inventor and futurist Ray Kurzweil. This was his first book and the Association of American Publishers named it the Most Outstanding Computer Science Book of 1990. It was reviewed in The New York Times and The Christian Science Monitor. The format is a combination of monograph and anthology with contributed essays by artificial intelligence experts such as Daniel Dennett, Douglas Hofstadter, and Marvin Minsky.
Kurzweil surveys the philosophical, mathematical and technological roots of artificial intelligence, starting with the assumption that a sufficiently advanced computer program could exhibit human-level intelligence. Kurzweil argues the creation of humans through evolution suggests that humans should be able to build something more intelligent than themselves. He believes pattern recognition, as demonstrated by vision, and knowledge representation, as seen in language, are two key components of intelligence. Kurzweil details how quickly computers are advancing in each domain.
Driven by the exponential improvements in computer power, Kurzweil believes artificial intelligence will be possible and then commonplace. He explains how it will impact all areas of people’s lives, including work, education, medicine, and warfare. As computers acquire human level faculties Kurzweil says people will be challenged to figure out what it really means to be human.
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